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Record W4403320024 · doi:10.1097/hc9.0000000000000524

Zinc supplementation to improve prognosis in patients with compensated advanced chronic liver disease: a multicenter, randomized, double-blind, placebo-controlled clinical trial

2024· article· en· W4403320024 on OpenAlexaff
Juan Bañares, Laia Aceituno, Lourdes Ruiz‐Ortega, Mònica Pons, Juan Abraldes, Joan Genescà

Bibliographic record

VenueHepatology Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDecompensationLiver transplantationInternal medicineChronic liver diseaseRandomized controlled trialPlaceboClinical trialNatural historyLiver diseaseMulticenter trialTransplantationCirrhosisGastroenterologyPathologyMulticenter study

Abstract

fetched live from OpenAlex

Zinc homeostasis could play a role in compensated advanced chronic liver disease, and its supplementation has been linked to improvement in liver function, a decrease of hepatic complications, and reduction in HCC incidence. Compensated advanced chronic liver disease encompasses a heterogeneous group of patients with variable risks of clinically significant portal hypertension and clinical events. The ANTICIPATE model is a validated model for stratifying these risks. Our aim is to demonstrate that zinc administration can reduce the rate and risk of presenting clinical events (first decompensation, HCC, death, and liver transplantation). This study protocol describes an ongoing phase III, national, multicenter, randomized, double-blind clinical trial that will enroll 300 patients to receive either the trial treatment (zinc acexamate) or placebo. An inclusion period of 42 months is planned, with a minimum follow-up of 2 years. Our principal hypothesis is that zinc could modify the natural history of patients with compensated advanced chronic liver disease, with an overall improvement in prognosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.370
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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